To facilitate scalable profiling of single cells, we developed split-pool ligation-based transcriptome sequencing (SPLiT-seq), a single-cell RNA-seq (scRNA-seq) method that labels the cellular origin of RNA through combinatorial barcoding. SPLiT-seq is compatible with fixed cells or nuclei, allows efficient sample multiplexing, and requires no customized equipment. We used SPLiT-seq to analyze 156,049 single-nucleus transcriptomes from postnatal day 2 and 11 mouse brains and spinal cords. More than 100 cell types were identified, with gene expression patterns corresponding to cellular function, regional specificity, and stage of differentiation. Pseudotime analysis revealed transcriptional programs driving four developmental lineages, providing a snapshot of early postnatal development in the murine central nervous system. SPLiT-seq provides a path toward comprehensive single-cell transcriptomic analysis of other similarly complex multicellular systems.

SUMMARY We present a consensus atlas of the human brain transcriptome in Alzheimer’s disease (AD), based on meta-analysis of differential gene expression in 2,114 postmortem samples. We discover 30 brain coexpression modules from seven regions as the major source of AD transcriptional perturbations. We next examine overlap with 251 brain differentially expressed gene sets from mouse models of AD and other neurodegenerative disorders. Human-mouse overlaps highlight responses to amyloid versus tau pathology and reveal age- and sex-dependent expression signatures for disease progression. Human coexpression modules enriched for neuronal and/or microglial genes broadly overlap with mouse models of AD, Huntington’s disease, amyotrophic lateral sclerosis, and aging. Other human coexpression modules, including those implicated in proteostasis, are not activated in AD models but rather following other, unexpected genetic manipulations. Our results comprise a cross-species resource, highlighting transcriptional networks altered by human brain pathophysiology and identifying correspondences with mouse models for AD preclinical studies.

52Alzheimer's disease (AD) is a complex and heterogenous brain disease that affects multiple inter-related 53 biological processes. This complexity contributes, in part, to existing difficulties in the identification of 54 successful disease-modifying therapeutic strategies. To address this, systems approaches are being used to 55 characterize AD-related disruption in molecular state. To evaluate the consistency across these molecular 56 models, a consensus atlas of the human brain transcriptome was developed through coexpression meta-57 analysis across the AMP-AD consortium. Consensus analysis was performed across five coexpression 58 methods used to analyze RNA-seq data collected from 2114 samples across 7 brain regions and 3 research 59 studies. From this analysis, five consensus clusters were identified that described the major sources of 60 AD-related alterations in transcriptional state that were consistent across studies, methods, and samples. 61AD genetic associations, previously studied AD-related biological processes, and AD targets under active 62 investigation were enriched in only three of these five clusters. The remaining two clusters demonstrated 63 strong heterogeneity between males and females in AD-related expression that was consistently observed 64 across studies. AD transcriptional modules identified by systems analysis of individual AMP-AD teams 65 were all represented in one of these five consensus clusters except ROS/MAP-identified Module 109, 66 which was specific for genes that showed the strongest association with changes in AD-related gene 67 expression across consensus clusters. The other two AMP-AD transcriptional analyses reported modules 68 that were enriched in one of the two sex-specific Consensus Clusters. The fifth cluster has not been 69 previously identified and was enriched for genes related to proteostasis. This study provides an atlas to 70 map across biological inquiries of AD with the goal of supporting an expansion in AD target discovery 71 efforts.

Constructing an atlas of cell types in complex organisms will require a collective effort to characterize billions of individual cells. Single cell RNA sequencing (scRNA-seq) has emerged as the main tool for characterizing cellular diversity, but current methods use custom microfluidics or microwells to compartmentalize single cells, limiting scalability and widespread adoption. Here we present Split Pool Ligation-based Transcriptome sequencing (SPLiT-seq), a scRNA-seq method that labels the cellular origin of RNA through combinatorial indexing. SPLiT-seq is compatible with fixed cells, scales exponentially, uses only basic laboratory equipment, and costs one cent per cell. We used this approach to analyze 109,069 single cell transcriptomes from an entire postnatal day 5 mouse brain, providing the first global snapshot at this stage of development. We identified 13 main populations comprising different types of neurons, glia, immune cells, endothelia, as well as types in the blood-brain-barrier. Moreover, we resolve substructure within these clusters corresponding to cells at different stages of development. As sequencing capacity increases, SPLiT-seq will enable profiling of billions of cells in a single experiment.Over three hundred years have passed since the discovery of the cell, yet we still do not have a complete catalogue of cell types or their functions. While transcriptomic profiling of individual cells has emerged as a promising solution to characterizing cellular diversity (1, 2), increases in throughput are needed before a complete "atlas" of cell types can be generated. Recent single cell RNA-seq (scRNA-seq) methods have profiled tens of thousands of individual cells (3-6), revealing new insights about the immune system (7) and identifying new cell types in the brain (8-11). However, since these methods require cell sorters and custom microfluidics or microwells, throughput is still limited, experiments are costly, and access is limited to a small number of labs.peer-reviewed)

Generative Adversarial Networks (GANs) have made releasing of synthetic images a viable approach to share data without releasing the original dataset. It has been shown that such synthetic data can be used for a variety of downstream tasks such as training classifiers that would otherwise require the original dataset to be shared. However, recent work has shown that the GAN models and their synthetically generated data can be used to infer the training set membership by an adversary who has access to the entire dataset and some auxiliary information. Current approaches to mitigate this problem (such as DPGAN [1]) lead to dramatically poorer generated sample quality than the original non–private GANs. Here we develop a new GAN architecture (privGAN), where the generator is trained not only to cheat the discriminator but also to defend membership inference attacks. The new mechanism is shown to empirically provide protection against this mode of attack while leading to negligible loss in downstream performances. In addition, our algorithm has been shown to explicitly prevent memorization of the training set, which explains why our protection is so effective. The main contributions of this paper are: i) we propose a novel GAN architecture that can generate synthetic data in a privacy preserving manner with minimal hyperparameter tuning and architecture selection, ii) we provide a theoretical understanding of the optimal solution of the privGAN loss function, iii) we empirically demonstrate the effectiveness of our model against several white and black–box attacks on several benchmark datasets, iv) we empirically demonstrate on three common benchmark datasets that synthetic images generated by privGAN lead to negligible loss in downstream performance when compared against non– private GANs. While we have focused on benchmarking privGAN exclusively on image datasets, the architecture of privGAN is not exclusive to image datasets and can be easily extended to other types of datasets. Repository link: https://github.com/microsoft/privGAN.

The temporal molecular changes that lead to disease onset and progression in Alzheimer’s disease (AD) are still unknown. Here we develop a temporal model for these unobserved molecular changes with a manifold learning method applied to RNA-Seq data collected from human postmortem brain samples collected within the ROS/MAP and Mayo Clinic RNA-Seq studies. We define an ordering across samples based on their similarity in gene expression and use this ordering to estimate the molecular disease stage–or disease pseudotime-for each sample. Disease pseudotime is strongly concordant with the burden of tau (Braak score, P = 1.0 × 10−5), Aβ (CERAD score, P = 1.8 × 10−5), and cognitive diagnosis (P = 3.5 × 10−7) of late-onset (LO) AD. Early stage disease pseudotime samples are enriched for controls and show changes in basic cellular functions. Late stage disease pseudotime samples are enriched for late stage AD cases and show changes in neuroinflammation and amyloid pathologic processes. We also identify a set of late stage pseudotime samples that are controls and show changes in genes enriched for protein trafficking, splicing, regulation of apoptosis, and prevention of amyloid cleavage pathways. In summary, we present a method for ordering patients along a trajectory of LOAD disease progression from brain transcriptomic data.

This paper focuses on the temperature control in a multi-zone building. The lumped heat transfer model based on thermal resistance and capacitance is used to analyze the system dynamics and control strategy. The resulting thermal network, including the zones, walls, and ambient environment, may be represented as an undirected graph. The thermal capacitances are the nodes in the graph, connected by thermal resistances as links. We assume the temperature measurements and temperature control elements (heating and cooling) are collocated. We show that the resulting input/output system is strictly passive, and any passive output feedback controller may be used to improve the transient and steady state performance without affecting the closed loop stability. The storage functions associated with passive systems may be used to construct a Lyapunov function, to demonstrate closed loop stability and motivates the construction of an adaptive feedforward control. A four-room example is included to illustrate the performance of the proposed passivity based control strategy.

We study joint estimation of the inverse temperature and magnetization parameters (β, B) of an Ising model with a non-negative coupling matrix An of size n × n, given one sample from the Ising model. We give a general bound on the rate of consistency of the bi-variate pseudolikelihood estimator. Using this, we show that estimation at rate n −1/2 is always possible if An is the adjacency matrix of a bounded degree graph. If An is the scaled adjacency matrix of a graph whose average degree goes to +∞, the situation is a bit more delicate. In this case estimation at rate n −1/2 is still possible if the graph is not regular (in an asymptotic sense). Finally, we show that consistent estimation of both parameters is impossible if the graph is Erdös-Renyi with parameter p > 0 free of n, thus confirming that estimation is harder on approximately regular graphs with large degree.